set_reasoning_budget

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the behavioral disclosure burden. It only says the tool returns a string instruction; it does not disclose whether the workspace_path is read, whether the operation is side-effect-free, or how the token budget affects behavior. The name set_reasoning_budget hints at mutation without confirming it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler, efficiently stating a transformation and its output. It loses some value because it omits a required input, but the structure itself is clear and compact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description needs to explain the return value and invocation context more fully. It only says 'string instruction,' omits workspace_path, contradicts the required token_budget, and gives no sense of what the agent should do with the returned instruction.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema documents all three parameters, so the baseline is 3. However, the description says token_budget is optional while the schema marks it required, and it entirely omits workspace_path, which actively misleads the agent about how to invoke the tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete transformation: take a task description and token budget, return a string instruction for adaptive reasoning. It is not a tautology and is distinguishable from the sibling tools, though it omits workspace_path and the tool name suggests a setter while the description describes a generator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case: when an AI needs a string instruction to reason adaptively. However, it provides no explicit guidance about when to prefer this tool over search_learnings, revert_reasoning_transaction, or log_reasoning_reflection, and no exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.